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pso algorithm
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    Empirical Study on Stock Through Arch Based on PSO Algorithm
    基于PSO的证券市场ARCH模型实证研究
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    The results of simulation indicate that modified PSO algorithm perform better than the genetic algorithm and the heuristic algorithm on the searching speed of global optimum,convergence speed and avoiding prematurity.
    通过仿真实例,改进微粒群算法的全局寻优、收敛性和克服早熟的能力均优于遗传、启发式算法。
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  pso algorithm
The ability of tracking changed PMD using PSO algorithm was also investigated.
      
The Particle Swarm Optimization (PSO) algorithm was introduced in adaptive PMD compensation.
      
Two-stage adaptive PMD compensation in 40 Gb/s OTDM optical communication system using PSO algorithm
      
Subsequently the Particle Swarm Optimization (PSO) algorithm is applied in order to select the "the best" ANN for the future investment decisions and to adapt the weights of other networks towards the weights of the best network.
      
The new methods are based on the hierarchical PSO (H-PSO) and a new type of H-PSO algorithm, called Partitioned Hierarchical PSO (PH-PSO).
      
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A modified particle swarm optimization (PSO) algorithm is proposed for vehicle routing problem (VRP). A PSO can solve a discrete combination optimization such as VRP by using random exchange sequence and PMX operator. A new integer-code method is adopted for the vehicle routing problem, and strained terms in VRP are processed by the penalty function. Simulation results demonstrated the effectiveness of this algorithm.

提出一种求解物流配送车辆路径问题的离散微粒群优化算法。通过引入随机交换序、PMX算子使微粒群优化算法能够求解车辆路径问题这类离散组合优化问题。设计了求解车辆路径问题一种新的整数编码方案,并采用罚函数法处理约束条件。计算结果表明,该算法是解决车辆路径问题的有效方法。

The problem of pricing for perishable commodities is mainly studied.According to the principle of profit maximization and based on a discrete demand function which can be represented as a negative binomial distribution,the optimal pricing model for the perishable products is established. Since the model involves some different stochastic distributions of several variables,which is difficult for the normal numerical methods to solve,the particle swarm optimization(PSO) algorithm is introduced for...

The problem of pricing for perishable commodities is mainly studied.According to the principle of profit maximization and based on a discrete demand function which can be represented as a negative binomial distribution,the optimal pricing model for the perishable products is established. Since the model involves some different stochastic distributions of several variables,which is difficult for the normal numerical methods to solve,the particle swarm optimization(PSO) algorithm is introduced for the first time to settle it,and a numerical example is studied then.The result indicated that: by using the PSO method,the optimal prices for different inventory levels can be derived quickly and effectively.In the end some possible extensions to the current study that need further investigations are presented.

对时效性商品的定价问题进行了研究。基于一种负二项分布的离散需求函数,并在利润最大化原则下,建立了时效商品最优定价模型。由于该模型涉及多个随机变量的概率分布,常规函数极值算法难以获得问题解析解,引入粒子群优化算法,对模型进行演化求解,并给出算例分析。结果表明:利用粒子群算法,可以快速有效得到不同库存量情况下应采取的最优定价。最后提出需要进一步解决的若干问题。

A discrete particle swarm optimization(PSO) algorithm was presented for Job Shop scheduling problem.In the algorithm,an operation-based representation was developed,and a new method was used to update position of particles with operation-based representation.So PSO can be easily applied to all classes of scheduling problems.But pure PSO may produce premature and poor results.Based on the complementary strengths of PSO and simulated annealing(SA) algorithm,four hybrid...

A discrete particle swarm optimization(PSO) algorithm was presented for Job Shop scheduling problem.In the algorithm,an operation-based representation was developed,and a new method was used to update position of particles with operation-based representation.So PSO can be easily applied to all classes of scheduling problems.But pure PSO may produce premature and poor results.Based on the complementary strengths of PSO and simulated annealing(SA) algorithm,four hybrid procedures were put forward by combining the PSO and SA.Numerical simulation demonstrates that within the framework of the newly designed hybrid algorithm,the NP-hard classic Job Shop scheduling problem can be efficiently solved with higher quality.

提出了一种离散粒子群调度算法,采用基于工序的编码方式及相应的位置和速度更新方法,使具有连续本质的粒子群算法直接适用于调度问题。针对粒子群算法容易陷入局部最优的缺陷,将其与模拟退火算法结合,得到了粒子群-模拟退火算法、改进的粒子群算法、粒子群-模拟退火交替算法以及粒子群-模拟退火协同算法等4种混合调度算法。仿真结果表明,混合算法均具有较高的求解质量。

 
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